In the area of automation, object localization stands as a fundamental functionalitywith widespread applicability. This master’s thesis focuses on a specificapplication involving robot object picking. Given recent advancements in depthcamera technology, there is a high interest in exploring the synergistic integrationof both 2D and 3D data to address challenges such as missing data, occlusion,varying viewing angles, and diverse lighting conditions. This master’s thesis presents the development of two distinct algorithms for arbitraryshaped template matching using 2D image features. Both algorithms leveragefeatures detected by the GoodFeaturesToTrack algorithm and described withScale-invariant feature transform (SIFT) descriptors. While an initial sliding windowmatcher was developed, it was ultimately discarded due to extensive timerequirements. Instead, a correspondence matcher was created, offering two variations:one exclusively employing 2D image data for matching and another utilizing3D coordinates to enhance matching accuracy. The correspondence matchingalgorithms showed similar strengths and weaknesses. They demonstrated proficiencyin handling scenarios characterized by occlusion, minor tilt, and varyingscaling. Both variations struggled with objects 90-degrees rotated and could inmany cases not find them. The findings suggest that the developed feature-based correspondence matchingalgorithm holds promise for object localization in industrial picking applications,although with limitations concerning objects with substantial rotationdifferences. Addressing the challenge of large rotations is recommended for enhancingthe algorithm’s robustness, followed by comprehensive testing to ascertainits efficacy in diverse scenarios.iii